System and method for deep-learning based estimation of coronary artery pressure drop

One or more non-transitory computer-readable media encoding one or more processor-executable routines, wherein the one or more routines, when executed by a processor, cause acts to be performed comprising:

• generating synthetic vessels;
• performing three-dimensional (3D) computational fluid dynamics (CFD) on the synthetic vessels for different flow rates to generate 3D CFD data;
• extracting 3D image patches from the synthetic vessels;
• obtaining pressure drops across the 3D image patches from the 3D CFD data;
• and
• training a deep neural network utilizing the 3D image patches, the pressure drops, and associated flow rates to generate a trained deep neural network.

A processor-based system, comprising:

• a memory encoding one or more processor-executable routines, wherein the routines, when executed cause acts to be performed comprising:
• generating synthetic vessels;
• performing three-dimensional (3D) computational fluid dynamics (CFD) on the synthetic vessels for different flow rates to generate 3D CFD data;
• extracting 3D image patches from the synthetic vessels;
• obtaining pressure drops across the 3D image patches from the 3D CFD data;
• and
• training a deep neural network utilizing the 3D image patches, the pressure drops, and associated flow rates to generate a trained deep neural network;
• and
• a processor configured to access and execute the one or more routines encoded by the memory.